โ๏ธAWS Machine Learning BlogโขStalecollected in 6m
Secure AI agents with Policy and Lambda interceptors
๐กLearn how to add robust security layers and dynamic validation to your Amazon Bedrock AI agents.
โก 30-Second TL;DR
What Changed
Implement deterministic access control using Policy configurations.
Why It Matters
This update significantly improves the security posture of enterprise AI agents by allowing developers to enforce granular, context-aware guardrails.
What To Do Next
Configure a Lambda interceptor in your Amazon Bedrock AgentCore gateway to validate user input against your specific compliance requirements.
Who should care:Developers & AI Engineers
Key Points
- โขImplement deterministic access control using Policy configurations.
- โขUtilize Lambda interceptors for real-time dynamic validation of agent requests.
- โขCombine both methods to enforce geography-based access control for enterprise AI agents.
๐ง Deep Insight
Web-grounded analysis with 12 cited sources.
๐ Enhanced Key Takeaways
- โขPolicy in Amazon Bedrock AgentCore utilizes Cedar, an open-source policy language, for defining authorization rules, and supports natural language authoring to simplify policy creation and validation, ensuring deterministic controls outside the agent's core logic.
- โขLambda interceptors provide flexible pre- and post-processing capabilities for agent requests and responses, enabling advanced functions such as payload enrichment, secure token exchange, schema translation, and comprehensive audit trail generation.
- โขAmazon Bedrock AgentCore Identity is a dedicated service for managing unique workload identities for AI agents, offering secure storage of credentials like OAuth tokens and API keys using AWS Key Management Service (KMS), and seamless integration with existing enterprise identity providers.
- โขThe Policy engine operates on a 'default-deny' security model, meaning any agent action not explicitly permitted by a defined policy will be blocked, thereby establishing a robust and auditable security posture.
- โขAgentCore Gateway serves as the central interception point for both Policy and Lambda interceptors, facilitating the transformation of existing APIs and AWS Lambda functions into agent-compatible tools and connecting to Model Context Protocol (MCP) servers for secure interactions.
๐ ๏ธ Technical Deep Dive
- Policy Language: Policies are constructed using Cedar, an open-source language for writing and enforcing authorization policies.
- Policy Engine: Developers create policy engines to store and manage Cedar policies, which are then associated with AgentCore Gateways.
- Interception Point: Policy in AgentCore intercepts all agent traffic through Amazon Bedrock AgentCore Gateways, evaluating each request against defined policies before allowing tool access.
- Lambda Interceptor Functionality: Lambda interceptors can be configured as request interceptors (processing incoming requests before reaching tools) or response interceptors (processing outgoing responses before returning to the agent). They can perform tasks like JWT token validation, user scope/permission checks, request enrichment, and schema translation.
- Identity Management: AgentCore Identity unifies Amazon Resource Names (ARNs) and agent identity, securely storing client credentials, API keys, and OAuth tokens using AWS KMS encryption. It supports OAuth 2.0 flows and integrates with services like Google, GitHub, Slack, Salesforce, and Okta.
- Gateway Capabilities: AgentCore Gateway transforms APIs and AWS Lambda functions into agent-compatible tools and connects to existing Model Context Protocol (MCP) servers. It supports IAM authorization in addition to OAuth for secure agent-to-tool interactions.
- Runtime Environment: AgentCore Runtime provides a secure, serverless environment for executing AI agents and their tools, supporting any framework, protocol, or model.
- Network Security: AgentCore services support Virtual Private Cloud (VPC) and AWS PrivateLink for enhanced enterprise security and infrastructure automation.
- Policy Authoring: Policies can be authored directly in Cedar or generated from natural language descriptions, with automated reasoning to validate safety conditions and identify overly permissive or restrictive rules.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
Enterprise adoption of AI agents in highly regulated industries will accelerate significantly.
The combination of deterministic policy enforcement and dynamic validation through interceptors provides the robust security and compliance framework necessary for sensitive enterprise workflows.
The use of the open-source Cedar language for policies will foster a broader community and ecosystem around AI agent security.
Standardizing on an open-source policy language can encourage wider adoption, tool development, and shared expertise in securing AI agent interactions.
AI agents will increasingly be treated as first-class, auditable entities within enterprise security and identity management frameworks.
The emphasis on specialized identity management (AgentCore Identity) and comprehensive audit trails for agent actions signifies a shift towards more rigorous governance for autonomous AI systems.
โณ Timeline
2023-04
Amazon Bedrock announced in preview.
2023-09
Amazon Bedrock generally available (GA).
2023-11
Agents for Amazon Bedrock generally available (GA).
2025-07
Amazon Bedrock AgentCore launched in preview.
2025-10
Amazon Bedrock AgentCore generally available (GA).
2025-12
Policy in Amazon Bedrock AgentCore launched in preview.
2026-03
Policy in Amazon Bedrock AgentCore generally available (GA).
๐ Sources (12)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
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Original source: AWS Machine Learning Blog โ